Experimental Data-Driven Flow Field Prediction for Compressor Cascade based on Deep Learning and ℓ1 Regularization

IF 1.8 3区工程技术 Q3 ENGINEERING, MECHANICAL

Journal of Thermal Science Pub Date : 2024-08-08 DOI:10.1007/s11630-024-2035-8

Tantao Liu, Limin Gao, Ruiyu Li

引用次数: 0

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基于深度学习和 ℓ1 正则化的压缩机级联实验数据驱动流场预测

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Thermal Science 工程技术-工程：机械

CiteScore

4.00

自引率

12.00%

发文量

188

审稿时长

3.0 months

期刊介绍： Founded in 1992, JOURNAL OF THERMAL SCIENCE, an international multi-disciplinary journal of energy and power sciences in English language sponsored by the Institute of Engineering Thermophysics, Chinese Academy of Sciences, publishes high quality papers of original work on experimental, numerical and theoretical investigations from contributors throughout the world. The scope of the Journal covers the following aspects: engineering thermodynamics, fluid mechanics, aerothermodynamics of internal flows, heat and mass transfer, combustion and reaction; sustainability of energy systems; energy conservation and storage; energy efficiency and climate change mitigation; renewable energy; nuclear energy; building, urban and distributed energy systems; cooling and refrigeration, heat pump; transport energy and emissions; operation, diagnostics and control of energy systems; energy policy and management. Review papers and scholarly research articles of high scientific/technical quality related to the development, advancement, and improved understanding of energy, power and environment sciences are sought. The Journal also incorporates technical notes, short communications, and discussions on previous published papers.